Analysis Techniques with SAS

Analysis Techniques with SAS

11/2/2012Jaydeep Modi1011113018

Multivariate TechniqueMultivariate Techniques are forms of statistical analysis that are used where there are two or more dependent variables to be analyzed simultaneously. These techniques are any statistical procedures that simultaneously analyze several measurements (variables). They encompass a range of analysis techniques which can examine quantitative data in more depth than can usually be obtained from a basic cross-analysis of the data.

Some Analysis techniques in SAS. Univeriate Regression Anova Correlation Neural network Forecasting Cluster Factor analysis

Univariate Analysis:-Univariate analysis is the simplest form of statistical analysis. Univariate analysis explores each variable in a data set, separately. It looks at the range of values, as well as the central tendency of the values. It describes the pattern of response to the variable. It describes each variable on its own.Univariate analysis commonly involves reporting measures of central tendency. This involves describing the way in which quantitative data tend to cluster around some value. In the univariate analysis, the measure of central tendency is an average of a set of measurements, the word average being variously construed as mean, median, mode or other measure of location, depending on the context. We can do the univariate analysis in the SAS Programming and the SAS code is under below:- PROC UNIVARIATE The proc univariate statement is required to invoke the univariate procedure. You can use the proc univariate statement by itself to request a variety of statistics for summarizing the data distribution of each analysis variable.Example:-

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Regression Analysis:-

Regression analysis is a statistical technique for estimating the relationships among variables. It includes many techniques for modeling and analyzing several variables, when the focus is on the relationship between a dependent variable and one or more independent variables.Regression analysis is widely used for prediction and forecasting. Regression analysis is also used to understand which among the independent variables are related to the dependent variable, and to explore the forms of these relationships.There are two basic regression models which are under below:- Linear Regression Non-Linear Regression

Linear Regression:- Linear regression is an approach to modeling the relationship between a scalar dependent variable y and one or more explanatory variables denoted X. Non-Linear Regression:- Nonlinear regression is a form of regression analysis in which observational data are modeled by a function which is a nonlinear combination of the model parameters and depends on one or more independent variables.

We can do the Regression analysis in the SAS Programming and the SAS code is under below:- PROC REG The proc reg statement is required. If you want to fit a model to the data, you must also use a model statement. If you want to use only the proc reg options, you do not need a model statement, but you must use a var statement.

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ANOVA:-Analysis of variance (ANOVA) is a collection of statistical models, and their associated procedures, in which the observed variance in a particular variable is partitioned into components attributable to different sources of variation.ANOVAs are useful in comparing two, three, or more means. The calculations of ANOVA can be characterized as computing a number of means and variances, dividing two variances and comparing the ratio to a handbook value to determine statistical significance. Calculating a treatment effect is then trivial, "the effect of any treatment is estimated by taking the difference between the mean of the observations which receive the treatment and the general mean.We can do the ANOVA in the SAS Programming and the SAS code is under below:-PROC ANOVA ;

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Correlation:-Correlation is a measure of relationship between two mathematical variables or measured data values. A correlation is a single number that describes the degree of relationship between two variables.We can do the Correlation in the SAS Programming and the SAS code is under below:-PROC CORR ; BY variables;FREQ variable;ID variables;PARTIAL variables;VAR variables;WEIGHT variable;WITH variables; The BY statement specifies groups in which separate correlation analyses are performed. The FREQ statement specifies the variable that represents the frequency of occurrence for other values in the observation. The ID statement specifies one or more additional tip variables to identify observations in scatter plots and scatter plot matrices. The PARTIAL statement identifies controlling variables to compute Pearson, Spearman, or Kendall partial-correlation coefficients. The VAR statement lists the numeric variables to be analyzed and their order in the correlation matrix. If you omit the VAR statement, all numeric variables not listed in other statements are used. The WEIGHT statement identifies the variable whose values weight each observation to compute Pearson product-moment correlation. The WITH statement lists the numeric variables with which correlations are to be computed.

The PROC CORR statement is the only required statement for the CORR procedure. The rest of this section provides detailed syntax information for each of these statements, beginning with the PROC CORR statement. The remaining statements are presented in alphabetical order.

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Forecasting:-Forecasting is the process of making statements about events whose actual outcomes (typically) have not yet been observed. The terms "forecast" and "forecasting" are sometimes reserved for estimates of values at certain specific future times.The Time Series Forecasting System is a point-and-click system that provides automatic model fitting and forecasting as well as interactive model development. The system provides a completely automatic forecasting model selection feature that selects the best-fitting model for each time series

Cluster Analysis:-Cluster analysis or clustering is the task of assigning a set of objects into groups so that the objects in the same cluster are more similar to each other than to those in other clusters.The purpose of cluster analysis is to place objects into groups, or clusters, suggested by the data, not defined a priori, such that objects in a given cluster tend to be similar to each other in some sense, and objects in different clusters tend to be dissimilar. You can also use cluster analysis to summarize data rather than to find "natural" or "real" clusters; this use of clustering is sometimes called dissection.We can do the Correlation in the SAS Programming and the SAS code is under below:-PROC CLUSTER ;VAR var1 var2 var3 ... var n;

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Factor Analysis:-Factor analysis is a statistical method used to describe variability among observed, correlated variables in terms of a potentially lower number of unobserved variables called factors.We can do the univariate analysis in the SAS Programming and the SAS code is under below:-PROC FACTOR ;

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Reference

www.google.com Support.sas.com A book named data analysis using Excel.